LP07 and LLC preclinical models of lung cancer induce divergent anabolic deficits and expression of pro-inflammatory effectors of muscle wasting | Journal of Applied Physiology

Pulmonology Department—Muscle Wasting and Cachexia in Chronic Respiratory Diseases and Lung Cancer Research Group, IMIM-Hospital del Mar, Barcelona, SpainNetwork of Excellence in Lung Diseases , Instituto de Salud Carlos III , Barcelona, Spain.

Pulmonology Department—Muscle Wasting and Cachexia in Chronic Respiratory Diseases and Lung Cancer Research Group, IMIM-Hospital del Mar, Barcelona, SpainNetwork of Excellence in Lung Diseases , Instituto de Salud Carlos III , Barcelona, Spain

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INPP5D limits plaque formation and glial reactivity in the APP/PS1 mouse model of Alzheimer’s diseaseThe dual specificity lipid/protein phosphatase SHIP1 (encoded by the INPP5D gene) is enriched in myeloid cells. Single nucleotide polymorphisms (SNPs) in INPP5D coding and non-coding regions impact risk for developing late onset sporadic Alzheimer’s disease (LOAD). We present pathological analyses with spatial transcriptomics of mice with tamoxifen-sensitive microglial knockdown of Inpp5d and show exacerbated plaque pathology, plaque-associated microglial density, and altered gene expression around plaques, suggesting novel markers for plaque-associated reactive microglia. Competing Interest Statement SAL is a founder of AstronauTx Ltd. All other others declare no competing interests.
Source: medical_xpress - 🏆 101. / 51 Read more »

CAR-T cell combination therapy: the next revolution in cancer treatment - Cancer Cell InternationalIn recent decades, the advent of immune-based therapies, most notably Chimeric antigen receptor (CAR)-T cell therapy has revolutionized cancer treatment. The promising results of numerous studies indicate that CAR-T cell therapy has had a remarkable ability and successful performance in treating blood cancers. However, the heterogeneity and immunosuppressive tumor microenvironment (TME) of solid tumors have challenged the effectiveness of these anti-tumor fighters by creating various barriers. Despite the promising results of this therapeutic approach, including tumor degradation and patient improvement, there are some concerns about the efficacy and safety of the widespread use of this treatment in the clinic. Complex and suppressing tumor microenvironment, tumor antigen heterogeneity, the difficulty of cell trafficking, CAR-T cell exhaustion, and reduced cytotoxicity in the tumor site limit the applicability of CAR-T cell therapy and highlights the requiring to improve the performance of this treatment. With this in mind, in the last decade, many efforts have been made to use other treatments for cancer in combination with tuberculosis to increase the effectiveness of CAR-T cell therapy, especially in solid tumors. The combination therapy results have promising consequences for tumor regression and better cancer control compared to single therapies. Therefore, this study aimed to comprehensively discuss different cancer treatment methods in combination with CAR-T cell therapy and their therapeutic outcomes, which can be a helpful perspective for improving cancer treatment in the near future.
Source: BioMedCentral - 🏆 22. / 71 Read more »

Adapting Pretrained Vision-Language Foundational Models to Medical Imaging DomainsMulti-modal foundation models are typically trained on millions of pairs of natural images and text captions, frequently obtained through web-crawling approaches. Although such models depict excellent generative capabilities, they do not typically generalize well to specific domains such as medical images that have fundamentally shifted distributions compared to natural images. Building generative models for medical images that faithfully depict clinical context may help alleviate the paucity of healthcare datasets. Thus, in this study, we seek to research and expand the representational capabilities of large pretrained foundation models to medical concepts, specifically for leveraging the Stable Diffusion model to generate domain specific images found in medical imaging. We explore the sub-components of the Stable Diffusion pipeline (the variational autoencoder, the U-Net and the text-encoder) to fine-tune the model to generate medical images. We benchmark the efficacy of these efforts using quantitative image quality metrics and qualitative radiologist-driven evaluations that accurately represent the clinical content of conditional text prompts. Our best-performing model improves upon the stable diffusion baseline and can be conditioned to insert a realistic-looking abnormality on a synthetic radiology image, while maintaining a 95% accuracy on a classifier trained to detect the abnormality. arxiv So,given I know what a neural network is,lemme see if I get this straight... Are you telling me that there is a'lack of diverse health care data to train students'BUT you have ENOUGH DATA(tens of thousands of pictures for a proper model)to TRAIN AN ALGORITHM !?!? The irony...🤡🌍
Source: medical_xpress - 🏆 101. / 51 Read more »